code: def _parse_args(args=None):
      max_world_size = torch.cuda.device_count() or 1
  
      parser = argparse.ArgumentParser(
          description=__doc__,
      )
      parser.add_argument(
          "--debug", action="store_true", help="Enable debug log"
      )
      group = parser.add_argument_group("Distributed Training")
      group.add_argument(
          "--worker-id",
          type=int,
          help=(
              "If not provided, the launched process serves as a master process of "
              "single-node, multi-worker training and spawns the worker subprocesses. "
              "If provided, the launched process serves as a worker process, which "
              "performs the actual training. The valid value is [0, --num-workers)."
          ),
      )
      group.add_argument(
          "--device-id",
          type=int,
          help="The CUDA device ID. Allowed only when --worker-id is provided.",
      )
      group.add_argument(
          "--num-workers",
          type=int,
          default=max_world_size,
          help=(
              "The size of distributed trainig workers. "
              "If launching a training as single-node, multi-worker training, "
              "(i.e. --worker-id is not provided) then this value should not exceed "
              "the number of available GPUs. "
              "If launching the training process as a multi-node, multi-gpu training, "
              "(i.e. --worker-id is provided) then the value has to match "
              f"the number of workers across nodes. (default: {max_world_size})"
          ),
      )
      group.add_argument(
          "--sync-protocol",
          type=str,
          default="env://",
          help=(
              "Synchronization protocol for distributed training. "
              "This value is passed as `init_method` argument of "
              "`torch.distributed.init_process_group` function."
              'If you are using `"env://"`, you can additionally configure '
              'environment variables "MASTER_ADDR" and "MASTER_PORT". '
              'If you are using `"file://..."`, then the process has to have '
              "the access to the designated file. "
              "See the documentation for `torch.distributed` for the detail. "
              'If you are running the training in a single node, `"env://"` '
              "should do. If you are running the training in multiple nodes, "
              "you need to provide the file location where all the nodes have "
              'access, using `"file://..."` protocol. (default: "env://")'
          ),
      )
      group.add_argument(
          "--random-seed",
          type=int,
          help="Set random seed value. (default: None)",
      )
      parser.add_argument(
          "rest",
          nargs=argparse.REMAINDER,
          help="Model-specific arguments.",
      )
      namespace = parser.parse_args(args)
      if namespace.worker_id is None:
          if namespace.device_id is not None:
              raise ValueError(
                  "`--device-id` cannot be provided when runing as master process."
              )
          if namespace.num_workers > max_world_size:
              raise ValueError(
                  "--num-workers ({num_workers}) cannot exceed {device_count}."
              )
      if namespace.rest[:1] == ["--"]:
          namespace.rest = namespace.rest[1:]
      return namespace
  
file_path: audio/examples/source_separation/train.py
imports: import argparse
  import logging
  import subprocess
  import sys
  import torch
  from utils import dist_utils
label_desc: Use image node_id 1 for calling a global function with example usage: _parse_args(args) and returns: namespace